{"id":"https://openalex.org/W7117120283","doi":"https://doi.org/10.48550/arxiv.2512.18046","title":"YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs","display_name":"YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs","publication_year":2025,"publication_date":"2025-12-19","ids":{"openalex":"https://openalex.org/W7117120283","doi":"https://doi.org/10.48550/arxiv.2512.18046"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.18046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.18046","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.18046","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048551866","display_name":"Ami Pandat","orcid":"https://orcid.org/0000-0002-6882-9881"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pandat, Ami","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121093970","display_name":"Punna Rajasekhar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajasekhar, Punna","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069702983","display_name":"Gopika Vinod","orcid":"https://orcid.org/0000-0003-4460-7341"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vinod, Gopika","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5069754989","display_name":"Rohit Shukla","orcid":"https://orcid.org/0000-0001-9367-9654"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shukla, Rohit","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5048551866"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.8730999827384949,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.8730999827384949,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0812000036239624,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.005799999926239252,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.805400013923645},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7153000235557556},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6158000230789185},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5600000023841858},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5522000193595886},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5270000100135803},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5162000060081482},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4350000023841858},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4309000074863434}],"concepts":[{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.805400013923645},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7153000235557556},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6618000268936157},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6158000230789185},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6157000064849854},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5600000023841858},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5522000193595886},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5270000100135803},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5162000060081482},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4154999852180481},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4101000130176544},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.374099999666214},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3154999911785126},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3034999966621399},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25380000472068787},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.18046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.18046","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.18046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.18046","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Unmanned":[0],"Aerial":[1],"Vehicles,":[2],"commonly":[3],"known":[4],"as,":[5],"drones":[6,24],"pose":[7],"increasing":[8],"risks":[9],"in":[10,131,154,242],"civilian":[11],"and":[12,17,34,56,84,118,129,150,157,161,179,245],"defense":[13],"settings,":[14],"demanding":[15],"accurate":[16,246],"real-time":[18,223,247],"drone":[19,98,224],"detection":[20,248],"systems.":[21],"However,":[22],"detecting":[23],"is":[25,46,78,89,103,211],"challenging":[26],"because":[27],"of":[28,41,75,95,142,159,189,199,204,231,249],"their":[29],"small":[30,67,97,136,250],"size,":[31],"rapid":[32],"movement,":[33],"low":[35],"visual":[36],"contrast.":[37],"A":[38,70],"modified":[39],"architecture":[40,102,122,146],"YolovN":[42],"called":[43],"the":[44,50,57,111,119,132,143,155,196,202,205,215,229,232,238],"YolovN-CBi":[45],"proposed":[47,101,120,144,149,233],"that":[48],"incorporates":[49],"Convolutional":[51],"Block":[52],"Attention":[53],"Module":[54],"(CBAM)":[55],"Bidirectional":[58],"Feature":[59],"Pyramid":[60],"Network":[61],"(BiFPN)":[62],"to":[63,66],"improve":[64],"sensitivity":[65],"object":[68,137],"detections.":[69],"curated":[71],"training":[72],"dataset":[73,88],"consisting":[74,94],"28K":[76],"images":[77,93],"created":[79],"with":[80,91,110,237],"various":[81],"flying":[82],"objects":[83],"a":[85,176,180,187,192],"local":[86,112],"test":[87,113],"collected":[90],"2500":[92],"very":[96],"objects.":[99],"The":[100,115,183,208],"evaluated":[104],"on":[105],"four":[106],"benchmark":[107],"datasets,":[108],"along":[109],"dataset.":[114],"baseline":[116,216],"Yolov5":[117],"Yolov5-CBi":[121],"outperform":[123],"newer":[124],"Yolo":[125],"versions,":[126],"including":[127],"Yolov8":[128],"Yolov12,":[130],"speed-accuracy":[133],"trade-off":[134],"for":[135,172,222],"detection.":[138,225],"Four":[139],"other":[140],"variants":[141,164],"CBi":[145,234],"are":[147,165],"also":[148],"evaluated,":[151],"which":[152],"vary":[153],"placement":[156],"usage":[158],"CBAM":[160],"BiFPN.":[162],"These":[163,226],"further":[166],"distilled":[167,184,209,239],"using":[168,175],"knowledge":[169],"distillation":[170,206],"techniques":[171],"edge":[173],"deployment,":[174],"Yolov5m-CBi":[177],"teacher":[178],"Yolov5n-CBi":[181],"student.":[182],"model":[185,210],"achieved":[186],"mA@P0.5:0.9":[188],"0.6573,":[190],"representing":[191],"6.51%":[193],"improvement":[194],"over":[195],"teacher's":[197],"score":[198],"0.6171,":[200],"highlighting":[201],"effectiveness":[203,230],"process.":[207],"82.9%":[212],"faster":[213],"than":[214],"model,":[217],"making":[218],"it":[219],"more":[220],"suitable":[221],"findings":[227],"highlight":[228],"architecture,":[235],"together":[236],"lightweight":[240],"models":[241],"advancing":[243],"efficient":[244],"UAVs.":[251]},"counts_by_year":[],"updated_date":"2025-12-24T23:14:05.333182","created_date":"2025-12-24T00:00:00"}